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32e5fcf 2e88e30 32e5fcf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 | """Celery tasks for background processing of character attribute extraction."""
import os
import logging
from typing import List, Dict, Any, Optional
from pathlib import Path
import json
from datetime import datetime
try:
from celery import Celery
from celery.result import AsyncResult
CELERY_AVAILABLE = True
except ImportError:
CELERY_AVAILABLE = False
Celery = None
AsyncResult = None
from character_pipeline import create_pipeline
from .pipeline.input_loader import DatasetItem
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Celery configuration
if CELERY_AVAILABLE:
# Redis broker configuration
REDIS_URL = os.getenv('REDIS_URL', 'redis://localhost:6379/0')
celery_app = Celery(
'character_extraction',
broker=REDIS_URL,
backend=REDIS_URL,
include=['celery_tasks']
)
# Celery configuration
celery_app.conf.update(
task_serializer='json',
accept_content=['json'],
result_serializer='json',
timezone='UTC',
enable_utc=True,
task_track_started=True,
task_time_limit=30 * 60, # 30 minutes
task_soft_time_limit=25 * 60, # 25 minutes
worker_prefetch_multiplier=1,
worker_max_tasks_per_child=1000,
)
else:
celery_app = None
logger.warning("Celery not available. Install with: pip install celery[redis]")
# Global pipeline instance (initialized per worker)
pipeline = None
def get_pipeline():
"""Get or create pipeline instance."""
global pipeline
if pipeline is None:
logger.info("Initializing pipeline in worker...")
pipeline = create_pipeline()
logger.info("Pipeline initialized successfully")
return pipeline
if CELERY_AVAILABLE:
@celery_app.task(bind=True, name='extract_single_image')
def extract_single_image(self, image_path: str, tags: Optional[str] = None):
"""Extract attributes from a single image."""
try:
self.update_state(state='PROGRESS', meta={'progress': 0, 'status': 'Loading pipeline...'})
pipeline = get_pipeline()
self.update_state(state='PROGRESS', meta={'progress': 25, 'status': 'Processing image...'})
# Extract attributes
attributes = pipeline.extract_from_image(image_path)
self.update_state(state='PROGRESS', meta={'progress': 75, 'status': 'Formatting results...'})
# Format result
result = {
'success': True,
'image_path': image_path,
'attributes': {
'age': getattr(attributes, 'age', None),
'gender': getattr(attributes, 'gender', None),
'ethnicity': getattr(attributes, 'ethnicity', None),
'hair_style': getattr(attributes, 'hair_style', None),
'hair_color': getattr(attributes, 'hair_color', None),
'hair_length': getattr(attributes, 'hair_length', None),
'eye_color': getattr(attributes, 'eye_color', None),
'body_type': getattr(attributes, 'body_type', None),
'dress': getattr(attributes, 'dress', None)
},
'confidence': getattr(attributes, 'confidence_score', 0.0),
'processed_at': datetime.now().isoformat()
}
self.update_state(state='PROGRESS', meta={'progress': 100, 'status': 'Completed'})
return result
except Exception as e:
logger.error(f"Error processing image {image_path}: {e}")
self.update_state(
state='FAILURE',
meta={'error': str(e), 'image_path': image_path}
)
raise
@celery_app.task(bind=True, name='batch_extract_images')
def batch_extract_images(self, image_paths: List[str], batch_size: int = 32,
use_hf_datasets: bool = True):
"""Extract attributes from multiple images in batch."""
try:
self.update_state(state='PROGRESS', meta={
'progress': 0,
'status': 'Initializing batch processing...',
'total_images': len(image_paths)
})
pipeline = get_pipeline()
# Create dataset items
items = [DatasetItem(image_path=path) for path in image_paths]
self.update_state(state='PROGRESS', meta={
'progress': 10,
'status': 'Created dataset items',
'total_images': len(items)
})
results = []
if use_hf_datasets and len(items) > batch_size:
# Use HuggingFace datasets for large batches
self.update_state(state='PROGRESS', meta={
'progress': 15,
'status': 'Using HuggingFace datasets for processing...'
})
def process_batch_hf(batch):
batch_results = []
for item_id, image_path in zip(batch['item_id'], batch['image_path']):
try:
attributes = pipeline.extract_from_image(image_path)
result = {
'success': True,
'item_id': item_id,
'image_path': image_path,
'attributes': {
'age': getattr(attributes, 'age', None),
'gender': getattr(attributes, 'gender', None),
'ethnicity': getattr(attributes, 'ethnicity', None),
'hair_style': getattr(attributes, 'hair_style', None),
'hair_color': getattr(attributes, 'hair_color', None),
'hair_length': getattr(attributes, 'hair_length', None),
'eye_color': getattr(attributes, 'eye_color', None),
'body_type': getattr(attributes, 'body_type', None),
'dress': getattr(attributes, 'dress', None)
},
'confidence': getattr(attributes, 'confidence_score', 0.0)
}
except Exception as e:
result = {
'success': False,
'item_id': item_id,
'image_path': image_path,
'error': str(e)
}
batch_results.append(result)
return {'results': batch_results}
# Process using HuggingFace datasets
processed_dataset = pipeline.input_loader.process_with_hf_map(
process_batch_hf,
items=items,
batch_size=batch_size,
num_proc=4
)
if processed_dataset:
for item in processed_dataset:
results.extend(item['results'])
# Update progress
progress = min(20 + (len(results) / len(items)) * 70, 90)
self.update_state(state='PROGRESS', meta={
'progress': progress,
'status': f'Processed {len(results)}/{len(items)} images',
'processed': len(results)
})
else:
# Use PyTorch DataLoader for smaller batches
self.update_state(state='PROGRESS', meta={
'progress': 15,
'status': 'Using PyTorch DataLoader for processing...'
})
dataloader = pipeline.input_loader.create_dataloader(
items=items,
batch_size=batch_size,
shuffle=False
)
for batch_idx, batch in enumerate(dataloader):
batch_results = []
for i, (item_id, image_path) in enumerate(zip(batch['item_ids'], batch['image_paths'])):
try:
attributes = pipeline.extract_from_image(image_path)
result = {
'success': True,
'item_id': item_id,
'image_path': image_path,
'attributes': {
'age': getattr(attributes, 'age', None),
'gender': getattr(attributes, 'gender', None),
'ethnicity': getattr(attributes, 'ethnicity', None),
'hair_style': getattr(attributes, 'hair_style', None),
'hair_color': getattr(attributes, 'hair_color', None),
'hair_length': getattr(attributes, 'hair_length', None),
'eye_color': getattr(attributes, 'eye_color', None),
'body_type': getattr(attributes, 'body_type', None),
'dress': getattr(attributes, 'dress', None)
},
'confidence': getattr(attributes, 'confidence_score', 0.0)
}
except Exception as e:
result = {
'success': False,
'item_id': item_id,
'image_path': image_path,
'error': str(e)
}
batch_results.append(result)
results.extend(batch_results)
# Update progress
progress = min(20 + (len(results) / len(items)) * 70, 90)
self.update_state(state='PROGRESS', meta={
'progress': progress,
'status': f'Processed batch {batch_idx + 1}/{len(dataloader)}',
'processed': len(results)
})
# Final processing
self.update_state(state='PROGRESS', meta={
'progress': 95,
'status': 'Finalizing results...'
})
# Calculate summary statistics
successful_results = [r for r in results if r.get('success', False)]
failed_results = [r for r in results if not r.get('success', False)]
avg_confidence = 0.0
if successful_results:
avg_confidence = sum(r['confidence'] for r in successful_results) / len(successful_results)
final_result = {
'success': True,
'total_processed': len(results),
'successful': len(successful_results),
'failed': len(failed_results),
'success_rate': len(successful_results) / len(results) * 100 if results else 0,
'average_confidence': avg_confidence,
'results': results,
'processed_at': datetime.now().isoformat(),
'processing_method': 'huggingface_datasets' if use_hf_datasets and len(items) > batch_size else 'pytorch_dataloader'
}
return final_result
except Exception as e:
logger.error(f"Error in batch processing: {e}")
self.update_state(
state='FAILURE',
meta={'error': str(e), 'total_images': len(image_paths)}
)
raise
@celery_app.task(bind=True, name='process_dataset_directory')
def process_dataset_directory(self, dataset_path: str, batch_size: int = 32,
max_images: Optional[int] = None):
"""Process all images in a dataset directory."""
try:
self.update_state(state='PROGRESS', meta={
'progress': 0,
'status': 'Discovering images in dataset...'
})
pipeline = get_pipeline()
# Configure input loader with dataset path
pipeline.input_loader.dataset_path = dataset_path
items = pipeline.input_loader.discover_dataset_items()
if max_images:
items = items[:max_images]
self.update_state(state='PROGRESS', meta={
'progress': 10,
'status': f'Found {len(items)} images',
'total_images': len(items)
})
if not items:
return {
'success': True,
'message': 'No images found in dataset directory',
'total_processed': 0
}
# Extract image paths
image_paths = [item.image_path for item in items]
# Use the batch processing task
return batch_extract_images.apply(
args=[image_paths, batch_size, True],
task_id=self.request.id
).get()
except Exception as e:
logger.error(f"Error processing dataset directory {dataset_path}: {e}")
self.update_state(
state='FAILURE',
meta={'error': str(e), 'dataset_path': dataset_path}
)
raise
else:
# Dummy functions when Celery is not available
def extract_single_image(*args, **kwargs):
raise NotImplementedError("Celery not available")
def batch_extract_images(*args, **kwargs):
raise NotImplementedError("Celery not available")
def process_dataset_directory(*args, **kwargs):
raise NotImplementedError("Celery not available")
# Utility functions for task management
def get_task_status(task_id: str) -> Dict[str, Any]:
"""Get the status of a Celery task."""
if not CELERY_AVAILABLE:
return {'error': 'Celery not available'}
result = AsyncResult(task_id, app=celery_app)
return {
'task_id': task_id,
'status': result.status,
'result': result.result,
'info': result.info,
'successful': result.successful(),
'failed': result.failed()
}
def cancel_task(task_id: str) -> Dict[str, Any]:
"""Cancel a Celery task."""
if not CELERY_AVAILABLE:
return {'error': 'Celery not available'}
celery_app.control.revoke(task_id, terminate=True)
return {
'task_id': task_id,
'status': 'cancelled'
}
def get_active_tasks() -> List[Dict[str, Any]]:
"""Get list of active tasks."""
if not CELERY_AVAILABLE:
return []
inspect = celery_app.control.inspect()
active_tasks = inspect.active()
if not active_tasks:
return []
tasks = []
for worker, task_list in active_tasks.items():
for task in task_list:
tasks.append({
'worker': worker,
'task_id': task['id'],
'name': task['name'],
'args': task['args'],
'kwargs': task['kwargs']
})
return tasks
if __name__ == '__main__':
if CELERY_AVAILABLE:
# Start Celery worker
celery_app.start()
else:
print("Celery not available. Install with: pip install celery[redis]") |